Palo Alto Research Center Inc.

Panasonic Corporation

Jungho Park

Ryan A Rossi

Ajay Raghavan

Tetsuyoshi Ogura

Akira Minegishi

Yosuke Tajika

One embodiment provides a system for facilitating anomaly detection. During operation, the system determines, by a computing device, a set of training instances, wherein a training instance represents a single class of data within a predefined range. The system computes a similarity score for each testing instance in a set of testing instances, wherein the similarity score is based on a similarity function which takes as input a respective testing instance and the set of training instances. The system determines a boundary threshold based on an ordering of the similarity score for each testing instance. The system classifies a first testing instance as an anomaly responsive to determining that the first testing instance falls outside the boundary threshold, thereby enhancing data mining and outlier detection in the single class of data using unlabeled training instances.

System and method for one-class similarity machines for anomaly detection

One embodiment provides a system for facilitating anomaly detection. During operation, the system determines, by a computing device, a set of testing data which includes a plurality of data points, wherein the set includes a data series for a first variable and one or more second variables, and wherein the one or more second variables are dependent on the first variable. The system divides the set of testing data into a number of groups based on a type of the data series. The system determines an inter-quartile range for a respective group. The system classifies a first testing data point in the respective group as an anomaly based on the inter-quartile range for the respective group, thereby enhancing data mining and outlier detection for the data series for multiple variables.

System and method for binned inter-quartile range analysis in anomaly detection of a data series

One embodiment provides a system for facilitating anomaly detection and characterization. During operation, the system determines, by a computing device, a first set of testing data which includes a plurality of data points, wherein the first set includes a data series for a first variable and one or more second variables. The system identifies anomalies by dividing the first set into a number of groups and performing an inter-quartile range analysis on data in each respective group. The system obtains, from the first set, a second set of testing data which includes a data series from a recent time period occurring before a current time, and which further includes a first data point from the identified anomalies. The system classifies the first data point as a first type of anomaly based on whether a magnitude of a derivative of the second set is greater than a first predetermined threshold.

Growing community of inventors

Gwangmyeong-si, South Korea

Jungho Park